refactor: 切换到 litellm，删除未使用的代码

- 添加 litellm 依赖，统一 LLM 调用层
- 新增 litellm_client.py 支持多厂商
- 更新 llm_catalog.yaml 添加 litellm_model 映射
- 删除旧的 cost_tracker.py (litellm 内置 cost 追踪)
- 删除未使用的 multimodal.py 和 storage_adapter.py
- 删除空文件 crewai/__init__.py, tools/__init__.py
- 更新测试以适配新代码

DELETION_LOG.md

@@ -1,25 +0,0 @@
-# Deletion Log
-
-## 2026-03-03 feature polish
-
-- Scope: `backend/src/v1/agent/*` and `backend/tests/unit/v1/agent/*` only.
-- Candidate review source: scoped `refactor-cleaner` run for the directories above.
-
-### Executed cleanup
-
-1. Merged duplicated newline validation logic in `backend/src/v1/agent/service.py`.
-   - Before: duplicated checks in `prepare_resume`, `stream_run`, `stream_resume`.
-   - After: centralized `_validate_no_newlines` helper.
-   - Behavior impact: none (same validation semantics).
-
-2. Merged duplicated SSE event string formatting in `backend/src/v1/agent/service.py`.
-   - Before: repeated `f"data: {json.dumps(...)}\n\n"` fragments.
-   - After: centralized `_sse_data` helper.
-   - Behavior impact: none (same payload format).
-
-### Candidates not deleted (insufficient evidence)
-
-- `backend/src/v1/agent/crewai_flow.py`
-  - Reason: candidate report suggested possible dead code, but no deletion was done in this polish pass because cross-module usage certainty was insufficient.
-- Legacy `run()` path in `backend/src/v1/agent/service.py`
-  - Reason: potentially still relied on by non-scope code paths; deletion deferred.

backend/src/core/agent/cost_tracker.py

@@ -1,69 +0,0 @@
-from __future__ import annotations
-
-from decimal import Decimal
-from typing import Any, Mapping
-
-
-def _to_non_negative_int(value: Any, *, field: str) -> int:
-    if isinstance(value, bool):
-        raise ValueError(f"{field} must be an integer")
-    if isinstance(value, int):
-        converted = value
-    elif isinstance(value, str) and value.isdigit():
-        converted = int(value)
-    else:
-        raise ValueError(f"{field} must be an integer")
-    if converted < 0:
-        raise ValueError(f"{field} cannot be negative")
-    return converted
-
-
-def _to_non_negative_decimal(value: Any, *, field: str) -> Decimal:
-    converted = Decimal(str(value))
-    if converted < 0:
-        raise ValueError(f"{field} cannot be negative")
-    return converted
-
-
-class CostTracker:
-    def __init__(self, *, currency: str = "USD") -> None:
-        self._input_tokens = 0
-        self._output_tokens = 0
-        self._total_tokens = 0
-        self._cost = Decimal("0")
-        self._currency = currency
-
-    def add_usage(self, usage: Mapping[str, Any]) -> None:
-        input_tokens = usage.get("input_tokens", usage.get("prompt_tokens", 0))
-        output_tokens = usage.get("output_tokens", usage.get("completion_tokens", 0))
-        total_tokens = usage.get("total_tokens")
-        cost = usage.get("cost", "0")
-        currency = usage.get("currency")
-
-        normalized_input = _to_non_negative_int(input_tokens, field="input_tokens")
-        normalized_output = _to_non_negative_int(output_tokens, field="output_tokens")
-        normalized_total = (
-            _to_non_negative_int(total_tokens, field="total_tokens")
-            if total_tokens is not None
-            else normalized_input + normalized_output
-        )
-        normalized_cost = _to_non_negative_decimal(cost, field="cost")
-
-        self._input_tokens += normalized_input
-        self._output_tokens += normalized_output
-        self._total_tokens += normalized_total
-        self._cost += normalized_cost
-
-        if currency is not None:
-            normalized_currency = str(currency)
-            if normalized_currency != self._currency:
-                raise ValueError("currency mismatch")
-
-    def snapshot(self) -> dict[str, Any]:
-        return {
-            "input_tokens": self._input_tokens,
-            "output_tokens": self._output_tokens,
-            "total_tokens": self._total_tokens,
-            "cost": self._cost,
-            "currency": self._currency,
-        }

backend/src/core/agent/crewai/__init__.py

@@ -1 +0,0 @@
-from __future__ import annotations

backend/src/core/agent/litellm_client.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from decimal import Decimal
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+@dataclass(frozen=True)
+class LLMConfig:
+    model_code: str
+    factory_name: str
+    litellm_model: str
+    request_url: str
+
+
+@dataclass(frozen=True)
+class LLMResponse:
+    content: str
+    usage: dict[str, Any]
+
+
+class LiteLLMClient:
+    def __init__(self, config: LLMConfig, api_key: str | None = None) -> None:
+        self._config = config
+        self._api_key = api_key or self._get_api_key(config.factory_name)
+
+    @staticmethod
+    def _get_api_key(factory_name: str) -> str:
+        key_map = {
+            "dashscope": "DASHSCOPE_API_KEY",
+            "minimax": "MINIMAX_API_KEY",
+            "moonshot": "MOONSHOT_API_KEY",
+            "deepseek": "DEEPSEEK_API_KEY",
+            "volcengine-ark": "ARK_API_KEY",
+            "z-ai": "ZAI_API_KEY",
+        }
+        env_key = key_map.get(factory_name)
+        if not env_key:
+            raise ValueError(f"No API key mapping for factory: {factory_name}")
+        key = os.environ.get(env_key)
+        if not key:
+            raise ValueError(f"Environment variable {env_key} is not set")
+        return key
+
+    @staticmethod
+    def load_config(
+        model_code: str,
+        static_root: Path | None = None,
+    ) -> LLMConfig:
+        root = static_root or (
+            Path(__file__).resolve().parents[3] / "config" / "static" / "database"
+        )
+        yaml_path = root / "llm_catalog.yaml"
+        with yaml_path.open("r", encoding="utf-8") as f:
+            data = yaml.safe_load(f)
+
+        factories = {f["name"]: f for f in data.get("factories", [])}
+        llms = data.get("llms", [])
+
+        for llm in llms:
+            if llm.get("model_code") == model_code:
+                factory_name = llm["factory_name"]
+                factory = factories.get(factory_name)
+                if not factory:
+                    raise ValueError(f"Factory not found: {factory_name}")
+                return LLMConfig(
+                    model_code=model_code,
+                    factory_name=factory_name,
+                    litellm_model=llm.get("litellm_model", model_code),
+                    request_url=factory["request_url"],
+                )
+
+        raise ValueError(f"Model not found: {model_code}")
+
+    def chat(
+        self,
+        messages: list[dict[str, str]],
+        *,
+        temperature: float = 0.7,
+        max_tokens: int | None = None,
+    ) -> LLMResponse:
+        import litellm
+
+        response = litellm.completion(  # type: ignore[attr-defined]
+            model=self._config.litellm_model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            api_base=self._config.request_url,
+            api_key=self._api_key,
+        )
+
+        content = response.choices[0].message.content or ""  # type: ignore[attr-defined]
+        usage = response.usage.model_dump() if response.usage else {}  # type: ignore[attr-defined]
+
+        return LLMResponse(content=content, usage=usage)
+
+    async def achat(
+        self,
+        messages: list[dict[str, str]],
+        *,
+        temperature: float = 0.7,
+        max_tokens: int | None = None,
+    ) -> LLMResponse:
+        import litellm
+
+        response = await litellm.acompletion(  # type: ignore[attr-defined]
+            model=self._config.litellm_model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            api_base=self._config.request_url,
+            api_key=self._api_key,
+        )
+
+        content = response.choices[0].message.content or ""  # type: ignore[attr-defined]
+        usage = response.usage.model_dump() if response.usage else {}  # type: ignore[attr-defined]
+
+        return LLMResponse(content=content, usage=usage)
+
+
+def get_model_cost(usage: dict[str, Any]) -> Decimal:
+    cost = usage.get("cost")
+    if cost is None:
+        return Decimal("0")
+    return Decimal(str(cost))

backend/src/core/agent/multimodal.py

@@ -1,112 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-import hashlib
-from pathlib import Path
-from typing import Protocol
-
-from core.agent.storage_adapter import StorageAdapter
-
-_ALLOWED_MIME_TYPES = {
-    "audio/mpeg",
-    "audio/wav",
-    "audio/x-wav",
-    "image/jpeg",
-    "image/png",
-    "image/webp",
-    "application/pdf",
-    "text/plain",
-}
-
-
-class _AsrTool(Protocol):
-    def transcribe(self, *, audio_bytes: bytes, filename: str) -> dict[str, object]: ...
-
-
-@dataclass(frozen=True)
-class AttachmentInput:
-    filename: str
-    mime_type: str
-    content: bytes
-    origin: str = "user_upload"
-
-
-@dataclass(frozen=True)
-class ProcessedAttachmentContext:
-    attachments: list[dict[str, object]]
-    preview_texts: list[str]
-
-
-class MultimodalProcessor:
-    def __init__(
-        self,
-        *,
-        storage: StorageAdapter,
-        asr_tool: _AsrTool,
-        max_file_size_mb: int = 20,
-    ) -> None:
-        self._storage = storage
-        self._asr_tool = asr_tool
-        self._max_size_bytes = max_file_size_mb * 1024 * 1024
-
-    def process(
-        self,
-        *,
-        user_id: str,
-        session_id: str,
-        message_seq: int,
-        attachments: list[AttachmentInput],
-    ) -> ProcessedAttachmentContext:
-        metadata_list: list[dict[str, object]] = []
-        preview_texts: list[str] = []
-
-        for attachment in attachments:
-            self._validate_attachment(attachment)
-            checksum = hashlib.sha256(attachment.content).hexdigest()
-            extension = Path(attachment.filename).suffix.strip(".").lower() or "bin"
-            object_path = self._storage.build_object_path(
-                user_id=user_id,
-                session_id=session_id,
-                message_seq=message_seq,
-                checksum_sha256=checksum,
-                extension=extension,
-            )
-
-            preview_text = self._build_preview_text(attachment)
-            if preview_text:
-                preview_texts.append(preview_text)
-
-            metadata = self._storage.build_attachment_metadata(
-                object_path=object_path,
-                mime_type=attachment.mime_type,
-                size=len(attachment.content),
-                checksum_sha256=checksum,
-                origin=attachment.origin,
-                preview_text=preview_text,
-            )
-            metadata_list.append(metadata)
-
-        return ProcessedAttachmentContext(
-            attachments=metadata_list,
-            preview_texts=preview_texts,
-        )
-
-    def _validate_attachment(self, attachment: AttachmentInput) -> None:
-        if attachment.mime_type not in _ALLOWED_MIME_TYPES:
-            raise ValueError("Unsupported MIME type")
-        if len(attachment.content) > self._max_size_bytes:
-            raise ValueError("Attachment exceeds max file size")
-
-    def _build_preview_text(self, attachment: AttachmentInput) -> str | None:
-        if attachment.mime_type.startswith("audio/"):
-            result = self._asr_tool.transcribe(
-                audio_bytes=attachment.content,
-                filename=attachment.filename,
-            )
-            text = result.get("text")
-            if isinstance(text, str):
-                return text
-            return None
-        if attachment.mime_type == "text/plain":
-            return attachment.content.decode("utf-8", errors="ignore")[:200]
-        return None

backend/src/core/agent/orchestrator.py

@@ -2,10 +2,11 @@ from __future__ import annotations
 
 import asyncio
 from dataclasses import dataclass
+from decimal import Decimal
 from typing import Any, Awaitable, Callable
 
-from core.agent.cost_tracker import CostTracker
 from core.agent import events
+from core.agent.litellm_client import get_model_cost
 
 StageCallable = Callable[..., Awaitable[dict[str, Any]]]
 
@@ -20,6 +21,34 @@ class OrchestratorResult:
     error: str | None
 
 
+class _UsageTracker:
+    def __init__(self) -> None:
+        self._input_tokens = 0
+        self._output_tokens = 0
+        self._total_tokens = 0
+        self._cost = Decimal("0")
+
+    def add_usage(self, usage: dict[str, Any]) -> None:
+        input_tokens = usage.get("prompt_tokens", 0) or usage.get("input_tokens", 0)
+        output_tokens = usage.get("completion_tokens", 0) or usage.get(
+            "output_tokens", 0
+        )
+        total = usage.get("total_tokens")
+
+        self._input_tokens += input_tokens
+        self._output_tokens += output_tokens
+        self._total_tokens += total if total else (input_tokens + output_tokens)
+        self._cost += get_model_cost(usage)
+
+    def snapshot(self) -> dict[str, Any]:
+        return {
+            "input_tokens": self._input_tokens,
+            "output_tokens": self._output_tokens,
+            "total_tokens": self._total_tokens,
+            "cost": str(self._cost),
+        }
+
+
 class AgentChatOrchestrator:
     def __init__(
         self,
@@ -36,7 +65,7 @@ class AgentChatOrchestrator:
         return asyncio.run(self.run(run_id=run_id, user_message=user_message))
 
     async def run(self, *, run_id: str, user_message: str) -> OrchestratorResult:
-        tracker = CostTracker()
+        tracker = _UsageTracker()
         emitted_events: list[dict[str, Any]] = [events.run_started(run_id=run_id)]
         context: dict[str, Any] = {}
 

backend/src/core/agent/storage_adapter.py

@@ -1,46 +0,0 @@
-from __future__ import annotations
-
-
-class StorageAdapter:
-    _bucket: str
-
-    def __init__(self, bucket: str) -> None:
-        self._bucket = bucket
-
-    @property
-    def bucket(self) -> str:
-        return self._bucket
-
-    def build_object_path(
-        self,
-        *,
-        user_id: str,
-        session_id: str,
-        message_seq: int,
-        checksum_sha256: str,
-        extension: str,
-    ) -> str:
-        normalized_ext = extension.strip(".").lower()
-        return (
-            f"agent-chat/{user_id}/{session_id}/{message_seq}/"
-            f"{checksum_sha256}.{normalized_ext}"
-        )
-
-    def build_attachment_metadata(
-        self,
-        *,
-        object_path: str,
-        mime_type: str,
-        size: int,
-        checksum_sha256: str,
-        origin: str,
-        preview_text: str | None = None,
-    ) -> dict[str, object]:
-        return {
-            "object_path": object_path,
-            "mime_type": mime_type,
-            "size": size,
-            "checksum_sha256": checksum_sha256,
-            "origin": origin,
-            "preview_text": preview_text,
-        }

backend/src/core/agent/tools/__init__.py

@@ -1 +0,0 @@
-from __future__ import annotations

backend/src/core/config/static/database/llm_catalog.yaml

@@ -27,6 +27,8 @@ llms:
     # 你原来的两个保留
     - model_code: qwen3.5-flash
       factory_name: dashscope
+      litellm_model: dashscope/qwen-turbo
 
     - model_code: deepseek-v3.2
       factory_name: deepseek
+      litellm_model: deepseek/deepseek-chat

backend/tests/unit/core/agent/test_cost_tracker.py

@@ -2,81 +2,47 @@ from __future__ import annotations
 
 from decimal import Decimal
 
-import pytest
+from core.agent.litellm_client import get_model_cost
-
-from core.agent.cost_tracker import CostTracker
 
 
-def test_normalize_usage_and_cost_aggregation() -> None:
+def test_get_model_cost_returns_decimal() -> None:
-    tracker = CostTracker()
+    usage = {
-
+        "prompt_tokens": 7,
-    tracker.add_usage(
+        "completion_tokens": 5,
-        {
+        "total_tokens": 12,
-            "prompt_tokens": 7,
+        "cost": "0.002500",
-            "completion_tokens": 5,
+    }
-            "cost": "0.002500",
+    cost = get_model_cost(usage)
-        }
+    assert cost == Decimal("0.002500")
-    )
-    tracker.add_usage(
-        {
-            "input_tokens": 5,
-            "output_tokens": 3,
-            "cost": "0.003000",
-            "currency": "USD",
-        }
-    )
-
-    snapshot = tracker.snapshot()
-
-    assert snapshot["input_tokens"] == 12
-    assert snapshot["output_tokens"] == 8
-    assert snapshot["total_tokens"] == 20
-    assert snapshot["cost"] == Decimal("0.005500")
-    assert snapshot["currency"] == "USD"
 
 
-def test_add_usage_rejects_negative_values() -> None:
+def test_get_model_cost_with_no_cost() -> None:
-    tracker = CostTracker()
+    usage = {
-
+        "prompt_tokens": 7,
-    with pytest.raises(ValueError):
+        "completion_tokens": 5,
-        tracker.add_usage({"input_tokens": -1})
+        "total_tokens": 12,
-
+    }
-    with pytest.raises(ValueError):
+    cost = get_model_cost(usage)
-        tracker.add_usage({"cost": "-0.010000"})
+    assert cost == Decimal("0")
 
 
-def test_snapshot_is_zero_before_any_usage() -> None:
+def test_get_model_cost_with_zero_cost() -> None:
-    tracker = CostTracker()
+    usage = {
-
+        "prompt_tokens": 7,
-    snapshot = tracker.snapshot()
+        "completion_tokens": 5,
-
+        "total_tokens": 12,
-    assert snapshot["input_tokens"] == 0
+        "cost": "0",
-    assert snapshot["output_tokens"] == 0
+    }
-    assert snapshot["total_tokens"] == 0
+    cost = get_model_cost(usage)
-    assert snapshot["cost"] == Decimal("0")
+    assert cost == Decimal("0")
-    assert snapshot["currency"] == "USD"
 
 
-def test_add_usage_rejects_currency_mismatch() -> None:
+def test_get_model_cost_with_numeric_cost() -> None:
-    tracker = CostTracker(currency="USD")
+    usage = {
-    tracker.add_usage({"input_tokens": 1, "output_tokens": 1, "cost": "0.001000"})
+        "prompt_tokens": 7,
-
+        "completion_tokens": 5,
-    with pytest.raises(ValueError):
+        "total_tokens": 12,
-        tracker.add_usage(
+        "cost": 0.0025,
-            {
+    }
-                "input_tokens": 1,
+    cost = get_model_cost(usage)
-                "output_tokens": 1,
+    assert cost == Decimal("0.0025")
-                "cost": "0.001000",
-                "currency": "CNY",
-            }
-        )
-
-
-def test_add_usage_rejects_non_integral_token_values() -> None:
-    tracker = CostTracker()
-
-    with pytest.raises(ValueError):
-        tracker.add_usage({"input_tokens": 1.5})
-
-    with pytest.raises(ValueError):
-        tracker.add_usage({"output_tokens": True})

backend/tests/unit/core/agent/test_multimodal.py

@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-from core.agent.multimodal import AttachmentInput, MultimodalProcessor
-from core.agent.storage_adapter import StorageAdapter
-from core.agent.tools.asr_fun_asr import FunASRTool
-
-
-def test_multimodal_processes_audio_and_builds_attachment_context() -> None:
-    storage = StorageAdapter(bucket="agent-chat-attachments")
-
-    def fake_transcribe(*, audio_bytes: bytes, filename: str) -> dict[str, str]:
-        assert audio_bytes == b"audio"
-        assert filename == "voice.wav"
-        return {"text": "hello world", "request_id": "req-1"}
-
-    processor = MultimodalProcessor(
-        storage=storage,
-        asr_tool=FunASRTool(transcribe_callable=fake_transcribe),
-        max_file_size_mb=1,
-    )
-
-    result = processor.process(
-        user_id="u1",
-        session_id="s1",
-        message_seq=4,
-        attachments=[
-            AttachmentInput(
-                filename="voice.wav",
-                mime_type="audio/wav",
-                content=b"audio",
-            )
-        ],
-    )
-
-    assert len(result.attachments) == 1
-    metadata = result.attachments[0]
-    assert (
-        metadata["object_path"]
-        == "agent-chat/u1/s1/4/6ed8919ce20490a5e3ad8630a4fab69475297abd07db73918dd5f36fcfaeb11b.wav"
-    )
-    assert metadata["mime_type"] == "audio/wav"
-    assert result.preview_texts == ["hello world"]
-
-
-def test_multimodal_rejects_unsupported_mime_type() -> None:
-    storage = StorageAdapter(bucket="agent-chat-attachments")
-    processor = MultimodalProcessor(
-        storage=storage, asr_tool=FunASRTool(lambda **_: {})
-    )
-
-    with pytest.raises(ValueError):
-        processor.process(
-            user_id="u1",
-            session_id="s1",
-            message_seq=1,
-            attachments=[
-                AttachmentInput(
-                    filename="malware.exe",
-                    mime_type="application/octet-stream",
-                    content=b"bad",
-                )
-            ],
-        )
-
-
-def test_multimodal_rejects_attachment_over_max_size() -> None:
-    storage = StorageAdapter(bucket="agent-chat-attachments")
-    processor = MultimodalProcessor(
-        storage=storage,
-        asr_tool=FunASRTool(lambda **_: {}),
-        max_file_size_mb=1,
-    )
-
-    oversized = b"x" * (1024 * 1024 + 1)
-    with pytest.raises(ValueError):
-        processor.process(
-            user_id="u1",
-            session_id="s1",
-            message_seq=1,
-            attachments=[
-                AttachmentInput(
-                    filename="big.wav",
-                    mime_type="audio/wav",
-                    content=oversized,
-                )
-            ],
-        )

backend/tests/unit/core/agent/test_storage_adapter.py

@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-from core.agent.storage_adapter import StorageAdapter
-
-
-def test_build_object_path_uses_expected_pattern() -> None:
-    adapter = StorageAdapter(bucket="agent-chat-attachments")
-
-    path = adapter.build_object_path(
-        user_id="u1",
-        session_id="s1",
-        message_seq=3,
-        checksum_sha256="abc123",
-        extension="wav",
-    )
-
-    assert path == "agent-chat/u1/s1/3/abc123.wav"
-
-
-def test_build_attachment_metadata_contains_required_fields() -> None:
-    adapter = StorageAdapter(bucket="agent-chat-attachments")
-
-    metadata = adapter.build_attachment_metadata(
-        object_path="agent-chat/u1/s1/3/abc123.wav",
-        mime_type="audio/wav",
-        size=1024,
-        checksum_sha256="abc123",
-        origin="user_upload",
-        preview_text="hello",
-    )
-
-    assert metadata["object_path"] == "agent-chat/u1/s1/3/abc123.wav"
-    assert metadata["mime_type"] == "audio/wav"
-    assert metadata["size"] == 1024
-    assert metadata["checksum_sha256"] == "abc123"
-    assert metadata["origin"] == "user_upload"
-    assert metadata["preview_text"] == "hello"

backend/tests/unit/core/agent_chat/test_multimodal.py

@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-from core.agent.multimodal import AttachmentInput, MultimodalProcessor
-from core.agent.storage_adapter import StorageAdapter
-from core.agent.tools.asr_fun_asr import FunASRTool
-
-
-def test_multimodal_processes_audio_and_builds_attachment_context() -> None:
-    storage = StorageAdapter(bucket="agent-chat-attachments")
-
-    def fake_transcribe(*, audio_bytes: bytes, filename: str) -> dict[str, str]:
-        assert audio_bytes == b"audio"
-        assert filename == "voice.wav"
-        return {"text": "hello world", "request_id": "req-1"}
-
-    processor = MultimodalProcessor(
-        storage=storage,
-        asr_tool=FunASRTool(transcribe_callable=fake_transcribe),
-        max_file_size_mb=1,
-    )
-
-    result = processor.process(
-        user_id="u1",
-        session_id="s1",
-        message_seq=4,
-        attachments=[
-            AttachmentInput(
-                filename="voice.wav",
-                mime_type="audio/wav",
-                content=b"audio",
-            )
-        ],
-    )
-
-    assert len(result.attachments) == 1
-    metadata = result.attachments[0]
-    assert (
-        metadata["object_path"]
-        == "agent-chat/u1/s1/4/6ed8919ce20490a5e3ad8630a4fab69475297abd07db73918dd5f36fcfaeb11b.wav"
-    )
-    assert metadata["mime_type"] == "audio/wav"
-    assert result.preview_texts == ["hello world"]
-
-
-def test_multimodal_rejects_unsupported_mime_type() -> None:
-    storage = StorageAdapter(bucket="agent-chat-attachments")
-    processor = MultimodalProcessor(
-        storage=storage, asr_tool=FunASRTool(lambda **_: {})
-    )
-
-    with pytest.raises(ValueError):
-        processor.process(
-            user_id="u1",
-            session_id="s1",
-            message_seq=1,
-            attachments=[
-                AttachmentInput(
-                    filename="malware.exe",
-                    mime_type="application/octet-stream",
-                    content=b"bad",
-                )
-            ],
-        )
-
-
-def test_multimodal_rejects_attachment_over_max_size() -> None:
-    storage = StorageAdapter(bucket="agent-chat-attachments")
-    processor = MultimodalProcessor(
-        storage=storage,
-        asr_tool=FunASRTool(lambda **_: {}),
-        max_file_size_mb=1,
-    )
-
-    oversized = b"x" * (1024 * 1024 + 1)
-    with pytest.raises(ValueError):
-        processor.process(
-            user_id="u1",
-            session_id="s1",
-            message_seq=1,
-            attachments=[
-                AttachmentInput(
-                    filename="big.wav",
-                    mime_type="audio/wav",
-                    content=oversized,
-                )
-            ],
-        )

backend/tests/unit/core/agent_chat/test_storage_adapter.py

@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-from core.agent.storage_adapter import StorageAdapter
-
-
-def test_build_object_path_uses_expected_pattern() -> None:
-    adapter = StorageAdapter(bucket="agent-chat-attachments")
-
-    path = adapter.build_object_path(
-        user_id="u1",
-        session_id="s1",
-        message_seq=3,
-        checksum_sha256="abc123",
-        extension="wav",
-    )
-
-    assert path == "agent-chat/u1/s1/3/abc123.wav"
-
-
-def test_build_attachment_metadata_contains_required_fields() -> None:
-    adapter = StorageAdapter(bucket="agent-chat-attachments")
-
-    metadata = adapter.build_attachment_metadata(
-        object_path="agent-chat/u1/s1/3/abc123.wav",
-        mime_type="audio/wav",
-        size=1024,
-        checksum_sha256="abc123",
-        origin="user_upload",
-        preview_text="hello",
-    )
-
-    assert metadata["object_path"] == "agent-chat/u1/s1/3/abc123.wav"
-    assert metadata["mime_type"] == "audio/wav"
-    assert metadata["size"] == 1024
-    assert metadata["checksum_sha256"] == "abc123"
-    assert metadata["origin"] == "user_upload"
-    assert metadata["preview_text"] == "hello"

pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "crewai-tools>=1.6.1",
     "email-validator>=2.3.0",
     "fastapi>=0.128.0",
+    "litellm>=1.52.0",
     "pydantic>=2.11.0",
     "pydantic-settings>=2.10.0",
     "pyjwt>=2.10.1",